Eosinophils and childhood asthma

Mediated Mendelian randomization analysis to determine the role of immune cells in regulating the effects of plasma metabolites on childhood asthma

Childhood asthma is a chronic inflammatory disease of the airways, the pathogenesis of which involves multiple factors including genetic predisposition, environmental exposure, and immune system regulation. To date, the causal relationships between immune cells, plasma metabolites, and childhood asthma remain undetermined. Therefore, we aim to utilize the Mendelian randomization approach to assess the causal relationships among immune cells, plasma metabolites, and childhood asthma. This study employed the Mendelian randomization approach to investigate how immune cells influenced the risk of childhood asthma by modulating the levels of plasma metabolites. Five Mendelian randomization methods-inverse variance weighted, weighted median, Mendelian randomization-Egger, simple mode, and weighted mode-were utilized to explore the causal relationships among 731 types of immune cells, 1400 plasma metabolites, and childhood asthma. The instrumental variables for the 731 immune cells and 1400 plasma metabolites were derived from a genome-wide association study meta-analysis. Additionally, sensitivity analyses were conducted to examine the robustness of the results, potential heterogeneity, and pleiotropy. The inverse variance weighted results indicated that HLA DR on dendritic cells (DC) is a risk factor for childhood asthma (OR: 1.08, 95% CI: 1.02-1.14). In contrast, HLA DR on DC acts as a protective factor against elevated catechol glucuronide levels (OR: 0.94, 95% CI: 0.91-0.98), while catechol glucuronide levels themselves serve as a protective factor for childhood asthma (OR: 0.73, 95% CI: 0.60-0.89). Thus, HLA DR on DC can exert a detrimental effect on childhood asthma through the negative regulation of catechol glucuronide levels. The mediating effect was 0.018, accounting for a mediation effect proportion of 23.4%. This study found that HLA DR on DC can exert a risk effect on childhood asthma through the negative regulation of catechol glucuronide levels, providing new strategies for the prevention and treatment of childhood asthma and guiding future research and clinical practice.

Current Challenges in Pediatric Asthma

Asthma is a chronic lung disease characterized by reversible bronchoconstriction and inflammation of the bronchi. Its increasing prevalence in childhood as well as different triggers make asthma a challenging disease in several ways: defining its phenotype/endotype, the diagnostic approach (especially in younger children), therapeutic options, and systematic follow-up. Considering these problems, this review approaches the current status and limitations of guidelines used for asthma management in children. It also emphasizes the key points which could lead to a better understanding and the direction to take in future studies.

Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization

OBJECTIVE

Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features.

METHODS

After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation.

RESULTS

Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%.

CONCLUSION

Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features.

Supplemental data for this article is available online at at www.tandfonline.com/ijas .

Recent advances in the treatment of childhood asthma: a clinical pharmacology perspective

INTRODUCTION

Childhood asthma is a complex heterogenous inflammatory disease that can pose a large burden on patients and their caregivers. There is a strong need to adapt asthma treatment to the individual patient taking into account underlying inflammatory profiles, moving from a 'one size fits all' approach toward a much-needed personalized approach.

AREAS COVERED

This review article aims to provide an overview of recent advances in the management and treatment of pediatric asthma, including novel insights on the molecular heterogeneity of childhood asthma, the emergence of biologicals to treat severe asthma, and innovative e-health and home monitoring techniques to make asthma management more convenient and accessible.

EXPERT OPINION

Molecular technologies have provided new treatment leads. E-health and home monitoring technologies have helped to gain more insights into disease dynamics and improve adherence to treatment while bringing health care to the patient. However, uncontrolled childhood asthma is still a major unmet clinical need and precision-medicine approaches are still scarce in clinical practice. Advanced omics methods may help researchers or clinicians to more accurately phenotype and treat subtypes of childhood asthma and gain more insight into the complexity of the disease.

Efficacy and safety of treatment with benralizumab for eosinophilic asthma

Eosinophilic asthma accounts for 40% to 50% of asthmatic patients. However, 5% to 10% of patients with asthma need high-dose drug control, which is clinically referred to as severe asthma patients. Interleukin (IL)-5 plays an important role in the proliferation, maturation, and migration of eosinophils. Benralizumab, as an antagonist of the IL-5 receptor, can treat eosinophilic asthma by promoting the apoptosis of eosinophils. The implications for efficacy and/or adverse events are unclear. This article reviews the findings about benralizumab in the treatment of severe eosinophilic asthma in recent years. Results indicated the effectiveness of benralizumab for the treatment of severe asthma.